MACHINE LEARNING FRAMEWORK FOR EARLY DETECTION OF FIRST-PARTY FRAUD IN CONSUMER CREDIT SYSTEMS

Abstract: First-party fraud has emerged as a significant challenge for consumer credit institutions, leading to substantial financial losses and increased operational risks. Traditional rule-based fraud detection systems often struggle to identify complex and evolving fraudulent behaviors at an early stage. This study proposes a machine learning framework for the early detection of first-party fraud in consumer credit systems by leveraging customer application data, credit history, transactional behavior, and repayment patterns. A hypothetical research methodology was developed involving data preprocessing, feature engineering, model training, and performance evaluation using multiple machine learning algorithms, including Logistic Regression, Random Forest, Neural Networks, and Gradient Boosting. The results indicate that the proposed framework significantly improves fraud detection performance, achieving higher accuracy, precision, recall, and ROC-AUC values compared to conventional approaches. Feature importance analysis highlights the critical role of behavioral and financial indicators in identifying fraudulent activities. Additionally, the integration of explainable artificial intelligence techniques enhances model transparency and supports effective decision-making. The findings suggest that machine learning-based fraud detection systems can provide financial institutions with a scalable, efficient, and proactive mechanism for reducing credit losses and strengthening fraud risk management in modern consumer credit environments.

Conclusion: This study demonstrates that a machine learning-based framework can significantly enhance the early detection of first-party fraud in consumer credit systems compared with traditional rule-based approaches. The hypothetical results indicate that advanced algorithms, particularly Gradient Boosting, achieve higher accuracy, precision, and recall while reducing false positives and detection time. The incorporation of behavioral, transactional, and credit-related features enables the identification of subtle fraud patterns that are often overlooked by conventional methods. Furthermore, the use of explainable AI techniques improves transparency and supports informed decision-making by risk analysts. Overall, the proposed framework provides a scalable, efficient, and data-driven solution for fraud prevention, helping financial institutions minimize credit losses, optimize investigative resources, and strengthen overall risk management practices in an increasingly digital lending environment.

Keywords: First-Party Fraud, Consumer Credit Systems, Machine Learning, Fraud Detection, Predictive Analytics, Credit Risk Management, Behavioral Analytics.

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